Single-cell and spatial transcriptomics

Soma-seq links intracellular protein states to transcriptional programs in the human brain

Soma-seq measures both RNA and intracellular proteins in single human brain cells, revealing protein states linked to transcriptional programs in Alzheimer's disease.

bMAE: Masked Autoencoder Latent Representations for Bulk RNA-seq Tissues

The paper introduces a masked autoencoder for bulk RNA-seq data to learn latent representations that outperform existing methods in tissue discrimination and structure preservation.

Scalable multimodal mapping of macrophage regulatory architecture by integrating optical and transcriptomic pooled screens

The paper introduces PerturbPair, a platform integrating transcriptomic and optical screens to map macrophage regulatory networks, revealing cross-modal relationships and disease-relevant gene programs.

Transcriptomics-Conditioned Virtual Tissue Synthesis via Diffusion Transformers

The paper introduces STMDiT, a diffusion transformer that synthesizes H&E histopathology images from transcriptomic data, improving image quality and transcriptomic fidelity over existing methods.

Cophenetic Spatial Topology Embedding reveals multiscale tissue architecture in spatial omics

COSTE embeds spatial relationships into a hierarchical metric space to reveal tissue architecture in spatial omics data, identifying spatially defined cell states and disease-associated patterns.

An organismal view of newborn cell dynamics in mammalian aging

The paper uses TrackerSci to analyze newborn cell dynamics across 21 mouse tissues at different ages, showing how aging affects cell renewal and composition.

TopOmics: Topic Modelling for All Omics

TopOmics introduces a flexible topic modeling framework for multi-omics data, demonstrating competitive performance and interpretability across diverse datasets.

MORPHE: Bridging Image Generation and Spatial Omics for Tissue Synthesis

MORPHE synthesizes biologically faithful tissue architecture from spatial-omics data, enabling reconstruction and extension of tissue context beyond experimental limitations.

CardioSeg: An interactive platform for integrated spatial transcriptomics data and nuclear morphological analysis of mouse heart tissue

CardioSeg provides an integrated platform for analyzing spatial transcriptomics and nuclear morphology in mouse heart tissue, achieving high accuracy in segmentation and revealing disease-specific changes.

NeuRoDev resolves lifelong temporal and cellular variation in human cortical gene expression

NeuRoDev provides a computational resource to analyze human cortical gene expression across development, revealing temporal and cellular variation in neuronal and glial cells.

Spatial-DC: a robust deep learning-based method for deconvolution of spatial proteomics

Spatial-DC improves spatial proteomics deconvolution by using deep learning to infer cell-type composition and reconstruct proteomic profiles from low-resolution data.

STID: A Standardized Spatial Transcriptomic Analysis Framework for Infectious Diseases

STID provides a standardized framework for analyzing spatial transcriptomic data in infectious diseases, enabling pathogen background correction, niche identification, and multi-sample comparative analyses.

Spatiotemporal transcriptome atlas of human embryos after gastrulation

The paper creates a spatiotemporal transcriptome atlas of human embryos after gastrulation, revealing organ-specific regulons and regulatory networks during development.

Signal, Bounds, and Baselines: Principles for Evaluating Virtual Cell Perturbation Models

This paper introduces SBB principles to evaluate virtual cell perturbation models, showing that complex methods often fail to outperform simple baselines.

Decoding the human PBMC isonome: Isoform-level resolution with single-cell long-read transcriptomics

This study uses long-read single-cell sequencing to analyze isoform-level diversity in human PBMCs, identifying novel isoforms and cell-type-specific patterns.

Scoring gene importance by interpreting single-cell foundation models

The paper introduces SIGnature, a framework for scoring gene importance using attributions from scRNA-seq foundation models, and demonstrates its utility in identifying novel associations of the MS1 signature with hyperinflammatory conditions.

Massively multiplex multimodal chemical screens at single-cell resolution

The paper introduces a framework for multimodal chemical screens combining RNA, protein, and epigenomic readouts, revealing compound-specific mechanisms and off-target effects in T cells.

ClusToRa: A niche-centric framework for identifying structural recruitment and infiltration in spatial omics

ClusToRa identifies structured cellular recruitment in spatial omics by comparing cluster densities to randomization, revealing niche-specific interactions in liver disease.

Cycle-consistent deep generative modeling unifies cellular states across unpaired spatial and single-cell modalities

MultiTME integrates spatial and single-cell data using a cycle-consistent deep generative model, revealing a spatially coherent tumor axis in colorectal cancer.

Spatially resolved, multimodal in vivo Perturb-seq using antibody-based cell hashing

This paper introduces PerturbSpace, a method combining CRISPR perturbations with spatially hashed single-cell multiomics to study gene function in tissue architecture.

Embryo-scale Visual Cell Sorting reveals a conserved transcriptomic signature of nucleolar size linked to proteostasis

The paper develops a high-throughput Visual Cell Sorting workflow to link nuclear compartment morphology to single-nucleus transcriptomes, identifying a conserved transcriptional signature of nucleolar size linked to proteostasis.

Unveiling Gene Modules at Atlas Scale through Hierarchical Clustering of Single-Cell Data

This paper introduces SuperSCC, a method for discovering cell identities and gene modules across datasets without integration, showing improved annotation accuracy and conserved immune cell status identification in lung microenvironments.

Decoding sequence determinants of gene expression in diverse cellular and disease states

The paper introduces Decima, a model that predicts cell type- and condition-specific gene expression from DNA sequence using single-cell RNA sequencing data.

MiTo: tracing the phenotypic evolution of somatic cell lineages via mitochondrial single-cell multi-omics

MiTo is a framework for analyzing mitochondrial single-cell lineage tracing data, outperforming existing methods in pre-processing and clonal inference, and providing insights into cell lineage and gene regulatory network heritability.

SOFisher: Reinforcement Learning-Guided Experiment Designs for Spatial Omics

SOFisher improves spatial omics experiment design by using reinforcement learning to guide FOV selection, outperforming conventional methods in capturing relevant biological features.

SpatialClaw: A Memory-Augmented Autonomous Ecosystem for Spatial Omics Analysis

SpatialClaw provides a memory-augmented system for spatial omics analysis, enabling end-to-end workflows through natural language interaction and outperforming standard models in benchmarking.

scDynOmics: An Optimized Transformer Model for Representation Learning from Single-Cell Multiomics

scDynOmics is a transformer model for single-cell multiomics data that outperforms existing models and provides interpretable insights into cellular dynamics.

Decoding Condition-Specific Cellular Crosstalk in Spatial Omics via Bilinear Edge Classification

CO_SCPLOWASEIC_SCPLOW identifies condition-specific cell-cell interactions in spatial omics data by modeling edges rather than cells, revealing spatial reorganization in disease states.

Decoding the causal drivers of spatial cellular topology

The paper introduces GLACIER, a method that infers spatially encoded transcriptional and signaling relationships using spatial Granger causality, applied to mouse cerebellum single-cell spatial transcriptomics data.

PerturbPlan: An analytical framework for designing Perturb-seq experiments

PerturbPlan provides an analytical framework to design single-cell CRISPR screens by calculating statistical power and enabling interactive experiment planning.